Description Usage Arguments Details Value Author(s) Examples

This function allows imputing missing values under the assumption that the distribution of complete values has to be Gaussian in each column.

Note that the imputed values are not necessary small values (compared to observed values).

1 | ```
impute.igcda(tab, tab.imp, conditions, q=0.95)
``` |

`tab` |
A numeric vector or matrix with observed and missing values. |

`tab.imp` |
A matrix where the missing values of |

`conditions` |
A vector of factors indicating the biological condition to which each column (experimental sample) belongs. |

`q` |
A quantile value (see Details). |

The mean and variance of the Gaussian distribution are determined using a linear regression between the quantiles of the observed values `q_{obs}`

and the ones of the standard normal distribution `q_{N(0,1)}`

.

The quantile value is used for determining the minimum of imputed values. This minimum is determined by the minimum observed value in the dataset minus `quant_diff(q)`

where `quant_diff(q)`

corresponds to a quantile value of the differences between the maximum and the minimum of the observed values for all the peptides in the condition. As a result, if `q`

is close to 1, `quant_diff(q)`

represents an extrem value between the maximum and the minimum of the intensity values in a condition for a peptide.

The numeric input matrix with imputed values. The distribution of the intensity values in each of its columns is supposed to be Gaussian.

Quentin Giai Gianetto <quentin2g@yahoo.fr>

1 2 3 4 5 6 7 8 | ```
#Simulating data
res.sim=sim.data(nb.pept=2000,nb.miss=600);
#Imputation of missing values with a MCAR-devoted algorithm: here the slsa algorithm
dat.slsa=impute.slsa(tab=res.sim$dat.obs,conditions=res.sim$condition,repbio=res.sim$repbio);
#Imputation of missing values under a Gaussian assumption
dat.gauss=impute.igcda(tab=res.sim$dat.obs, tab.imp=dat.slsa, conditions=res.sim$conditions);
``` |

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